For many years, information technology (IT) organizations (the “providers”) have offered IT management services and computing resources to other business entities (the “customers”). In a “traditional” service model, the customers share a provider's management services, but each customer purchases or leases specific resources for the customer's exclusive benefit. The customer may purchase or lease the resources directly from the provider or from a third party. Regardless of their origins, though, such a purchase or lease may require extensive, time-consuming negotiations based upon the customer's anticipated requirements. If the customer's requirements are less than anticipated, then the customer effectively has wasted resources. If, however, the customer's requirements are greater than anticipated, then the customer may have to enter into additional time-consuming negotiations for the necessary resources.
Alternatives to the traditional service model, though, are able to anticipate and meet customers' processing needs as their requirements grow, while maximizing existing resources. One such alternative, pioneered by International Business Machines Corporation, allows a service provider to allocate resources to customers “on-demand” as the customers' needs change. In this on-demand service model, customers share computing and networking resources. In one implementation of the on-demand model, a service provider creates “logical” partitions of computing resources on primary processing units (commonly known as “mainframe” computers). Typically, an on-demand service provider contracts with several customers to provide a certain level of service to each customer, and creates a logical partition (LPAR) of resources for each customer to fulfill its obligations. Unlike traditional service contracts, an on-demand service contract generally requires that the customer be billed only for resources actually used, and for fixed costs not directly related to usage (such as labor costs incurred in support of the contract).
In an on-demand data center, software is shared, simultaneously serving multiple customers in a flexible, automated fashion. The software is standardized, requiring little customization, and it is scalable, providing capacity on demand in a pay-as-you-go model. The software can be stored on a shared file system accessible from one or more servers. The software is executed via transactions that contain data and server processing requests that use processing resources on the accessed server. The accessed server also may make requests of other servers that require the use of processing resources. The use or consumption of processing resources is measured in units of time such as minutes, seconds, or hours. A CPU is one example of a processing resource, but other resources that may be consumed and measured include (but are not limited to) network bandwidth, memory, storage, packet transfers, complete transactions, etc.
In the on-demand service environment, problems arise when voids or inaccuracies occur in the metered data. Such voids or inaccuracies create incorrect calculations. Correcting the incorrect calculations requires processing delays and causes lost revenue. Therefore, significant time must be expended to validate data integrity and to resolve issues that are discovered in the validation process. Incorrect calculations discovered during post processing procedures result in late submissions for bills and reports. In order to resolve the incorrect calculation, the on-demand service administrator must discover the time of origin of the data issue, remove the erroneous data from the on demand metering, reporting and billing system, reprocess the data, and revalidate the outputs. Such corrective procedures result in late billing and reporting, as well as significant time expenditures to resolve and validate the data integrity.
A need exists for a way to identify problems in on-demand service data as the data is generated so that problems can be resolved in close temporal proximity to the time of origin. Moreover, the need extends to both on-demand service providers, and to on-demand service clients to view usage of metered services in real time, and to be alerted to problems.